arXiv research

A measure called relative cluster entropy distinguishes between correlated and uncorrelated sequences.

New algorithms for clustering and dimension reduction using relative von Neumann entropy.

New clustering method adapts to data structure.

Entropy measure quantifies volatility correlation and risk diversity in asset portfolios.

We investigate the relative information efficiency of financial markets by measuring the entropy of the time series of high frequency data. Our tool to measure efficiency is the Shannon entropy, applied to 2-symbol and 3-symbol discretisations of the data. Analysing 1-minute and 5-minute price time series of 55 Exchang…

Study on relative entropy for hypersurfaces in hyperbolic space.

This study uses moving average cluster entropy to analyze financial market dynamics.

Improved clustering accuracy with disentangled latent code representation.

Extends specific relative entropy to multidimensional continuous martingales.

Entropy asymmetry affects regularization in ERM, leading to biased solutions.

Market dynamic is quantified in terms of the entropy $S(τ,n)$ of the clusters formed by the intersections between the series of the prices $p_t$ and the moving average $\widetilde{p}_{t,n}$. The entropy $S(τ,n)$ is defined according to Shannon as $\sum P(τ,n)\log P(τ,n),$ with $P(τ,n)$ the probability for the cluster t…

EDD uses entropy of distance distributions to cluster unlabeled data.

We define a relative entropy for two expanding solutions to mean curvature flow of hypersurfaces, asymptotic to the same cone at infinity. Adapting work of White and using recent results of Bernstein and Bernstein-Wang, we show that expanders with vanishing relative entropy are unique in a generic sense. This also impl…

New statistical test for change-point detection using relative entropy.

Study on the limits of learning HMM parameters under various conditions.

Inference for normal and Monte Carlo distributions using minimum relative entropy.

Entropy regularization improves interpretability of probabilistic clustering models.

The notion of utility maximising entropy (u-entropy) of a probability density, which was introduced and studied by Slomczynski and Zastawniak (Ann. Prob 32 (2004) 2261-2285, arXiv:math.PR/0410115 v1), is extended in two directions. First, the relative u-entropy of two probability measures in arbitrary probability space…

New regularization method reduces support of empirical risk minimization solutions.

Characterizes pseudo-Anosov mapping classes using cluster algebra techniques.

Unique continuation result for expanding Ricci solitons.

We introduce a class of generalized relative entropies (inspired by the Bregman divergence in information theory) on the Wasserstein space over a weighted Riemannian or Finsler manifold. We prove that the convexity of all the entropies in this class is equivalent to the combination of the nonnegative weighted Ricci cur…

We investigate the $m$-relative entropy, which stems from the Bregman divergence, on weighted Riemannian and Finsler manifolds. We prove that the displacement $K$-convexity of the $m$-relative entropy is equivalent to the combination of the nonnegativity of the weighted Ricci curvature and the $K$-convexity of the weig…

New algorithm solves saddle point problems in Banach spaces.

The R Package CEC performs clustering based on the cross-entropy clustering (CEC) method, which was recently developed with the use of information theory. The main advantage of CEC is that it combines the speed and simplicity of $k$-means with the ability to use various Gaussian mixture models and reduce unnecessary cl…

We present a new method of generating mixture models for data with categorical attributes. The keys to this approach are an entropy-based density metric in categorical space and annealing of high-entropy/low-density components from an initial state with many components. Pruning of low-density components using the entro…

New method estimates robust multi-period portfolios using entropy.

Earlier studies have shown that stock market distributions can be well described by distributions derived from Tsallis entropy, which is a generalization of Shannon entropy to non-extensive systems. In this paper, Tsallis relative entropy (TRE), which is the generalization of Kullback-Leibler relative entropy (KLRE) to…

This paper presents a new fuzzy k-means algorithm for the clustering of high-dimensional data in various subspaces. Since high-dimensional data, some features might be irrelevant and relevant but may have different significance in the clustering process. For better clustering, it is crucial to incorporate the contribut…

Long memory and volatility clustering are two stylized facts frequently related to financial markets. Traditionally, these phenomena have been studied based on conditionally heteroscedastic models like ARCH, GARCH, IGARCH and FIGARCH, inter alia. One advantage of these models is their ability to capture nonlinear dynam…

Entropy analysis via kernel methods for probabilistic inference.

A $q$-Gaussian measure is a generalization of a Gaussian measure. This generalization is obtained by replacing the exponential function with the power function of exponent $1/(1-q)$ ($q\neq 1$). The limit case $q=1$ recovers a Gaussian measure. For $1\leq q <3$, the set of all $q$-Gaussian densities over the real line …

Study quantifies information flow in neural networks using relative entropy and RG analogy.

This paper optimizes trading strategies to minimize risk and maximize profit while accounting for market uncertainty.

Two entropy measures quantify suboptimal portfolio performance.

We study a notion of relative entropy motivated by self-expanders of mean curvature flow. In particular, we obtain the existence of this quantity for arbitrary hypersurfaces trapped between two disjoint self-expanders asymptotic to the same cone. This allows us to begin to develop the variational theory for the relativ…

The relative $α$-entropy is the Rényi analog of relative entropy and arises prominently in information-theoretic problems. Recent information geometric investigations on this quantity have enabled the generalization of the Cramér-Rao inequality, which provides a lower bound for the variance of an estimator of an escort…

We introduce a pathwise approach to analyze the relative performance of an equity portfolio with respect to a benchmark market portfolio. In this energy-entropy framework, the relative performance is decomposed into three components: a volatility term, a relative entropy term measuring the distance between the portfoli…

Paper proves Jeffrey's update rule minimizes relative entropy.

A new clustering method using Bayesian techniques improves robustness and interpretability.

A new method, REC, compresses images by encoding their latent representations efficiently.

Abstract: Necessary and sufficient conditions for gradient flows of relative entropy in Lindblad equations.

Proposes HypCSE for enhanced hierarchical clustering.

In this paper we focus on the uniqueness question for (expanding) solutions of the Harmonic map flow coming out of smooth 0-homogeneous maps with values into a closed Riemannian manifold. We introduce a relative entropy for two purposes. On the one hand, we prove the existence of two expanding solutions associated to a…

reval package selects best clustering solutions via stability-based validation.

REMEDI improves neural entropy estimation across various tasks.

The null energy condition is characterized via convexity of entropy in Lorentzian manifolds.

We introduce a property of mutation loops, called the sign stability, with a focus on an asymptotic behavior of the iteration of the tropical $\mathcal{X}$-transformation. A sign-stable mutation loop has a numerical invariant which we call the cluster stretch factor, in analogy with that of a pseudo-Anosov mapping clas…